where, \(F\)  and \(M\)  are fitness and mutation potentials, respectively.
The gradient functions (denoted by \(\nabla F\)  and \(\nabla M\) ) determine the forces acting on the phenotypes by selection and mutation, respectively [1]}. \({\text{d}}{\bf W}\)  is a differential that the reflects the stochastic effect of genetic drift by a multi-dimensional Wiener noise process [2]}. The amplitude of the noise is proportional to \(\Sigma \) , which is the square root of the covariance matrix (i.e., \(\Sigma ^\top \Sigma \equiv K\) ), scaled by the effective population size \(N\)  that adjusts the overall strength of the noise. In other words, the fluctuations of the mean phenotype across realizations of an evolutionary process is proportional to the intra-population variance \(K\)  and inversely scales with the effective population size (i.e., the sample size) \(N\) .
